arXiv subjects

Jiayu Feng

Publications and source records attributed to Jiayu Feng.

2 recordsLinked to original sources

arXiv2026

Second-harmonic generation from an optically levitated KTP nanocrystal in vacuum

The optically levitated system in vacuum has emerged as a powerful platform for studies of fundamental physics and precision measurements. Although various nanoparticles have been successfully levitated in vacuum, they typically lack the capability to support optical nonlinear processes. Here, we experimentally demonstrate the stable levitation of a potassium titanyl phosphate (KTP) nonlinear nanocrystal in vacuum and investigate its second-harmonic generation (SHG) properties. This levitated system intrinsically provides a pristine dark-background environment with a high signal-to-noise ratio. The trapping laser simultaneously serves as a fundamental light for efficient SHG. Moreover, the polarization of the collected SHG signal is correlated with that of the fundamental laser, providing clear evidence of the optical torque enabling controllable alignment of the nanocrystal with the driving field. Our work establishes a new route toward exploring nonlinear optical processes in vacuum levitation systems and designing novel nanodevices with high manipulation agility in a fully contact-free environment.

physics.optics↗

arXiv2025

U-SWIFT: A Unified Surface Wave Inversion Framework with Transformer via Normalization of Dispersion Curves

Deep learning is an increasingly popular approach for inverting surface wave dispersion curves to obtain Vs profiles. However, its generalizability is constrained by the depth and velocity scales of training data. We propose a unified deep learning framework that overcomes this limitation via normalization of dispersion curves. By leveraging the scaling properties of dispersion curves, our approach enables a single, pre-trained model to predict Vs profiles across diverse scales, from shallow subsurface (e.g., < 10 m depth) to crustal levels. The framework incorporates a novel transformer-based model to handle variable-length dispersion curves and removes tedious manual parameterization. Results from synthetic and field data demonstrate that it delivers rapid and robust inversions with uncertainty estimates. This work provides an efficient inversion approach applicable to a wide spectrum of applications, from near-surface engineering to crustal imaging. The framework establishes a paradigm for developing scale-invariant deep learning models in geophysical inversion.

physics.geo-ph↗